ifelse(!dir.exists(outdir), dir.create(outdir), FALSE)
[1] TRUE
model.cca <- readRDS("~/models/modelCCA_reference_hvg_PBMC_SCElist_20191105.RDS")
model.liger <- readRDS("~/models/modelLiger_reference_hvg_PBMC_SCElist_20191105.RDS")
model.conos <- readRDS("~/models/modelConos_reference_hvg_PBMC_SCElist_20191105.RDS")
seu.cca <- readRDS("~/models/labelTransferCCA_reference_hvg_PBMC_SCElist_20191105.RDS")
# seu.cca.union <- readRDS("~/models/labelTransferCCA_PBMC_SCElist_20191105.RDS")
seu.liger <- readRDS("~/models/labelTransferLiger_reference_hvg_PBMC_SCElist_20191105.RDS")
seu.conos <- readRDS("~/models/labelTransferConos_reference_hvg_PBMC_SCElist_20191105.RDS")
integrate_features <- model.cca$model@anchor.features
int.list <- list(CCA=seu.cca, Liger=seu.liger, Conos=seu.conos)
## Make method color palette
method.palette <- brewer_palette_4_values(names(int.list), "Set1")

Embeddings

Visualize label transfer on original ATAC data (embedded SnapATAC bins)

## Load original data
orig.ATAC <- readRDS("~/my_data/10X_data/atac_pbmc_10k_nextgem.snapATAC.RDS")
sce.list <- readRDS("~/my_data/10X_data/PBMC_SCElist_20191105.RDS")
orig.RNA <- sce.list$RNA
Loading required package: SingleCellExperiment
Loading required package: SummarizedExperiment
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply,
    parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:dplyr’:

    combine, intersect, setdiff, union

The following object is masked from ‘package:Matrix’:

    which

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match,
    mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames,
    sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min

Loading required package: S4Vectors

Attaching package: ‘S4Vectors’

The following objects are masked from ‘package:dplyr’:

    first, rename

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    expand

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    expand

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    expand.grid

Loading required package: IRanges

Attaching package: ‘IRanges’

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    collapse, desc, slice

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    reduce

Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor,
    see 'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: DelayedArray
Loading required package: matrixStats

Attaching package: ‘matrixStats’

The following objects are masked from ‘package:Biobase’:

    anyMissing, rowMedians

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    count

Loading required package: BiocParallel

Attaching package: ‘DelayedArray’

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    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

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    simplify

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    aperm, apply, rowsum


Attaching package: ‘SummarizedExperiment’

The following object is masked from ‘package:Seurat’:

    Assays
## Make SeuratObjects
atac.seu <- snapToSeurat(
    obj=orig.ATAC, 
    eigs.dims=1:20, 
    norm=TRUE,
    scale=TRUE
    )
Epoch: checking input parameters ... 
Non-unique features (rownames) present in the input matrix, making uniqueFeature names cannot have underscores ('_'), replacing with dashes ('-')Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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atac.seu <- RenameCells(atac.seu, new.names = orig.ATAC@metaData$barcode)
## Add cell type predictions
getPredictedLabels <- function(seu.int, int.name, id.col="predicted.id", score.col="score"){
  pred.df <- seu.int$ATAC@meta.data[,c(id.col, score.col), drop=F] 
  rownames(pred.df) <- str_remove(rownames(pred.df), "^ATAC_")
  colnames(pred.df) <- c(str_c("predicted.id", "_", int.name), str_c("score", "_", int.name))
  pred.df
  }
pred.cca <- getPredictedLabels(seu.cca, "CCA", score.col = "prediction.score.max")
pred.liger <- getPredictedLabels(seu.liger, "Liger")
pred.conos <- getPredictedLabels(seu.conos, "Conos")
# pred.cca.union <- getPredictedLabels(seu.cca.union, "CCA.union", score.col = "prediction.score.max")
# cbind(pred.cca, pred.cca.union)
if (all(rownames(pred.conos) == rownames(pred.cca)) & all(rownames(pred.conos) == rownames(pred.liger))) {
  atac.seu <- AddMetaData(atac.seu, metadata = cbind(pred.cca, pred.liger, pred.conos))
} else {
  stop("Non corresponding cell names")
}

plotly::ggplotly(pl)
geom_GeomTextRepel() has yet to be implemented in plotly.
  If you'd like to see this geom implemented,
  Please open an issue with your example code at
  https://github.com/ropensci/plotly/issues
orig.RNA.seu <- as.Seurat(orig.RNA)
orig.RNA.seu <- FindVariableFeatures(orig.RNA.seu)
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
orig.RNA.seu <- ScaleData(orig.RNA.seu)
Centering and scaling data matrix

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orig.RNA.seu <- RunPCA(orig.RNA.seu)
PC_ 1 
Positive:  LTB, CD3E, TRAC, CD3D, TRBC2, LCK, CD3G, IL32, ETS1, IL7R 
       TCF7, CD247, CD27, CD7, BCL11B, CD69, CD2, ISG20, SPOCK2, TRBC1 
       GZMM, ARL4C, LAT, FCMR, CCR7, RORA, SYNE2, RASGRP1, LINC00861, LEF1 
Negative:  CSTA, MNDA, SERPINA1, FGL2, FCN1, NCF2, CPVL, CD68, MPEG1, CST3 
       MS4A6A, CD14, CLEC7A, CD36, AC020656.1, SLC7A7, VCAN, CSF3R, TGFBI, CFD 
       TNFAIP2, CYBB, KLF4, SPI1, GRN, S100A12, TNFSF13B, KCTD12, CFP, TIMP2 
PC_ 2 
Positive:  ANXA1, IL32, CD3E, CD247, S100A4, LCK, TMSB4X, GZMM, CD7, CD3D 
       S100A10, CD3G, ITGB2, TRBC1, TRAC, CD2, IL7R, LAT, KLRB1, RORA 
       GZMA, ZAP70, CTSW, GAPDH, NEAT1, BCL11B, CST7, S100A6, PRF1, SAMD3 
Negative:  IGHM, BANK1, CD79A, SPIB, FAM129C, MS4A1, CD22, IGHD, TSPAN13, LINC00926 
       BCL11A, HLA-DQA1, VPREB3, TNFRSF13C, TCL1A, BLNK, BLK, RALGPS2, COBLL1, JCHAIN 
       MZB1, HLA-DQB1, FCRL5, FCRLA, HLA-DQA2, CD79B, AFF3, FCRL1, TCF4, FCRL2 
PC_ 3 
Positive:  LTB, CCR7, IL7R, FOSB, MAL, TRABD2A, LEF1, CD3G, TCF7, CD3D 
       CD27, VIM, SLC2A3, COTL1, TRAC, BIRC3, CAMK4, TSHZ2, PASK, NOSIP 
       ZFP36L2, FHIT, FOS, NCF1, INPP4B, MYC, MS4A1, NELL2, TNFRSF13C, ADTRP 
Negative:  GZMB, CLIC3, KLRF1, KLRD1, NKG7, PRF1, GNLY, SPON2, CST7, FGFBP2 
       TRDC, GZMA, ADGRG1, CCL4, C12orf75, GZMH, MATK, TBX21, PTGDR, CCL5 
       S1PR5, HOPX, CD160, AKR1C3, PTCRA, FCGR3A, TTC38, RHOC, MYOM2, SLAMF7 
PC_ 4 
Positive:  GZMB, KLRD1, CYBA, KLRF1, PRF1, CLIC3, NKG7, FGFBP2, SPON2, GNLY 
       CST7, MT-CO1, GZMA, CCL4, ADGRG1, TRDC, HOPX, MATK, GZMH, TBX21 
       FCGR3A, S1PR5, CD160, MYOM2, AKR1C3, TTC38, IL2RB, XCL2, APOBEC3G, SH2D1B 
Negative:  TUBB1, GP9, CMTM5, CLDN5, TREML1, TMEM40, CTTN, CAVIN2, CLEC1B, ITGA2B 
       PF4, GNG11, C2orf88, ACRBP, AC147651.1, HIST1H2BJ, CLU, ARHGAP6, SH3BGRL2, GMPR 
       AP001189.1, PDZK1IP1, GNAZ, AC090409.1, SMOX, SPARC, MPIG6B, ESAM, SCN1B, MYL9 
PC_ 5 
Positive:  SCT, CLEC4C, LILRA4, LRRC26, TNFRSF21, SCAMP5, PHEX, KCNK17, PACSIN1, SCN9A 
       SMPD3, DNASE1L3, CYP46A1, RHEX, TPM2, MAP1A, SERPINF1, SHD, LAMP5, IL3RA 
       LINC00996, PPM1J, ASIP, AL096865.1, AC097375.1, SMIM5, CIB2, GAS6, ZFAT, CUX2 
Negative:  MS4A1, LINC00926, CD22, CD79A, BANK1, PDLIM1, CD79B, IGHD, VPREB3, TNFRSF13C 
       KLRF1, FGFBP2, KLRD1, FCRL1, RALGPS2, ADGRG1, FCER2, GNLY, FCRL2, TRDC 
       CCL4, HOPX, PAX5, CD72, FCRL5, PRF1, CST7, ADAM28, PTGDR, SWAP70 
orig.RNA.seu <- RunUMAP(orig.RNA.seu, dims=1:30)
15:52:11 UMAP embedding parameters a = 0.9922 b = 1.112
15:52:12 Read 5607 rows and found 30 numeric columns
15:52:12 Using Annoy for neighbor search, n_neighbors = 30
15:52:12 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:52:13 Writing NN index file to temp file /tmp/RtmpXEhLFb/file9c191c14dd
15:52:13 Searching Annoy index using 1 thread, search_k = 3000
15:52:14 Annoy recall = 100%
15:52:16 Commencing smooth kNN distance calibration using 1 thread
15:52:18 Initializing from normalized Laplacian + noise
15:52:19 Commencing optimization for 500 epochs, with 240156 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:52:33 Optimization finished
DimPlot(orig.RNA.seu, group.by="annotation", label = TRUE)

Prediction score

Quantifies the uncertainty of the prediction. Calculated differently for every method, but used to define which cells are “unassigned”.

ggarrange(predict_score_hist, predict_score_cumedist, common.legend = TRUE, widths = c(0.8, 1.2),
          labels=c("A", "B")) +
  ggsave(paste0(outdir, "prediction_score_distribution.pdf"), height = 6, width = 10)
Removed 180 rows containing non-finite values (stat_ecdf).

Cell type composition

Compare cell type fractions (w uncertainty)

pred.labels.df %>%
  group_by(method) %>%
  drop_na() %>%
  mutate(tot.cells=n()) %>%
  ungroup() %>%
  group_by(method, predicted.id) %>%
  summarise(tot.label = n(), tot.cells = max(tot.cells), mean.score=mean(score)) %>%
  mutate(frac.label=tot.label/tot.cells) %>%
  # bind_rows(orig.frac.df) %>%
  ggplot(aes(method, predicted.id)) +
  geom_point(aes(color=mean.score, size=frac.label)) +
  scale_color_continuous() +
  scale_color_gradient(low ="grey", high="blue") +
  theme_classic(base_size = 16)
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

Does the uncertainty depend on the size of the cluster?

original size?

pred.labels.df %>%
  group_by(method) %>%
  drop_na() %>%
  mutate(tot.cells=n()) %>%
  ungroup() %>%
  group_by(method, predicted.id) %>%
  summarise(tot.label = n(), tot.cells = max(tot.cells), mean.score=median(score), sd.score=mad(score)) %>%
  mutate(frac.label=tot.label/tot.cells) %>%
  left_join(select(orig.frac.df, predicted.id, frac.label), by="predicted.id", suffix=c(".int",".original")) %>%
  # bind_rows(orig.frac.df) %>%
  ggplot(aes(frac.label.original, mean.score, color=method)) +
  geom_point(size=2) +
  geom_errorbar(aes(ymin=mean.score-sd.score, ymax=mean.score+sd.score), alpha=0.6) +
  scale_color_brewer(palette="Set1") +
  facet_grid(.~method) +
  theme_bw(base_size = 16)
Column `predicted.id` joining character vector and factor, coercing into character vector

Scoring per cell type

dodge <- position_dodge(width=0.9)
pred.labels.df %>%
  group_by(method, predicted.id) %>%
  summarise(score.mean=mean(score, na.rm=F), score.cv = sd(score)/mean(score), n=n()) %>%
  # ggplot(aes(predicted.id, score.mean, fill=method)) +
  # geom_col( position = dodge) +
  ggplot(aes(predicted.id, method)) +
  geom_point(aes(size=score.mean, color=score.mean)) +
  scale_color_gradient(low="white", high = "blue") +
  # geom_errorbar(aes(ymin=score.mean-score.cv, ymax=score.mean+score.cv), position=dodge, width=0.25)  +
  coord_flip() +
  theme_bw(base_size = 16)

pred.labels.df %>%
  ggplot(aes(predicted.id, score, color=method)) +
  geom_boxplot() +
  coord_flip() +
  scale_color_brewer(palette="Set1")

Agreement with unsupervised clustering of ATAC data

Calculate which fractions of NNs in bin based graph of ATAC cells have the same annotation

full_join(pred.labels.df, knn_score_df) %>%
  ggplot(aes(KNN_score, color=method)) +
  stat_ecdf() +
  facet_wrap("predicted.id") +
  scale_color_brewer(palette = "Set1") +
  coord_fixed()
Joining, by = c("cell", "method")

map(cell.types, ~ ggarrange(plot_KNNecdf(.x), UMAPs_cluster(.x), nrow = 2, heights = c(1,0.8)))
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

[[7]]

[[8]]

[[9]]

[[10]]

[[11]]

[[12]]

[[13]]

Which cells are inconsistently aligned?

Closer look at Liger

Compare feature selection strategy

ggarrange(predict_score_hist, predict_score_cumedist, common.legend = TRUE, widths = c(0.8, 1.2),
          labels=c("A", "B")) 
Removed 251 rows containing non-finite values (stat_bin).Removed 251 rows containing non-finite values (stat_bin).Removed 411 rows containing non-finite values (stat_ecdf).

Thoughts

  • Conos scores a lot of cells with high confidence, but fails to assign cells to difficult clusters
  • CCA resembles the composition of the RNA data better, but curious that the other methods identify way more
---
title: "Label transfer EDA"
output: html_notebook
---


```{r}
library(Seurat)
library(SnapATAC)
library(tidyverse)
library(DescTools)  # 4 AUC function
library(glue)
library(ggalluvial)  # 4 river plot
library(ggpubr)
source("~/multiOmic_benchmark/utils.R")

gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

## Make output directory
outdir <- "~/multiOmic_benchmark/output/20191106_labelTransferEDA_PBMC/"
ifelse(!dir.exists(outdir), dir.create(outdir), FALSE)

```


```{r}
model.cca <- readRDS("~/models/modelCCA_reference_hvg_PBMC_SCElist_20191105.RDS")
model.liger <- readRDS("~/models/modelLiger_reference_hvg_PBMC_SCElist_20191105.RDS")
model.conos <- readRDS("~/models/modelConos_reference_hvg_PBMC_SCElist_20191105.RDS")

seu.cca <- readRDS("~/models/labelTransferCCA_reference_hvg_PBMC_SCElist_20191105.RDS")
seu.liger <- readRDS("~/models/labelTransferLiger_reference_hvg_PBMC_SCElist_20191105.RDS")
seu.conos <- readRDS("~/models/labelTransferConos_reference_hvg_PBMC_SCElist_20191105.RDS")

integrate_features <- model.cca$model@anchor.features

int.list <- list(CCA=seu.cca, Liger=seu.liger, Conos=seu.conos)

## Make method color palette
method.palette <- brewer_palette_4_values(names(int.list), "Set1")

```

### Embeddings
Visualize label transfer on original ATAC data (embedded SnapATAC bins)
```{r}
## Load original data
orig.ATAC <- readRDS("~/my_data/10X_data/atac_pbmc_10k_nextgem.snapATAC.RDS")
sce.list <- readRDS("~/my_data/10X_data/PBMC_SCElist_20191105.RDS")
orig.RNA <- sce.list$RNA

## Make SeuratObjects
atac.seu <- snapToSeurat(
    obj=orig.ATAC, 
    eigs.dims=1:20, 
    norm=TRUE,
    scale=TRUE
    )
atac.seu <- RenameCells(atac.seu, new.names = orig.ATAC@metaData$barcode)

## Add cell type predictions
getPredictedLabels <- function(seu.int, int.name, id.col="predicted.id", score.col="score"){
  pred.df <- seu.int$ATAC@meta.data[,c(id.col, score.col), drop=F] 
  rownames(pred.df) <- str_remove(rownames(pred.df), "^ATAC_")
  colnames(pred.df) <- c(str_c("predicted.id", "_", int.name), str_c("score", "_", int.name))
  pred.df
  }

pred.cca <- getPredictedLabels(seu.cca, "CCA", score.col = "prediction.score.max")
pred.liger <- getPredictedLabels(seu.liger, "Liger")
pred.conos <- getPredictedLabels(seu.conos, "Conos")

# pred.cca.union <- getPredictedLabels(seu.cca.union, "CCA.union", score.col = "prediction.score.max")
# cbind(pred.cca, pred.cca.union)


if (all(rownames(pred.conos) == rownames(pred.cca)) & all(rownames(pred.conos) == rownames(pred.liger))) {
  atac.seu <- AddMetaData(atac.seu, metadata = cbind(pred.cca, pred.liger, pred.conos))
} else {
  stop("Non corresponding cell names")
}
```

```{r, fig.height=8, fig.width=18}
## make cell type palette
cell.types <- levels(seu.cca$RNA$annotation)
cell.type.pal <- setNames(gg_color_hue(length(cell.types)), cell.types)

atac.seu <- RunUMAP(atac.seu, reduction = "SnapATAC", reduction.name = "umap.snap", dims=1:20)

ggpubr::ggarrange(
  plotlist = list(
    DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_CCA"  , cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("CCA"),
    DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_Liger", cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("Liger"),
    DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_Conos", cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("Conos")
  ),
  common.legend = TRUE, ncol=3, nrow=1
)
```

```{r}
pl <-     DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_Liger", cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("Conos")
plotly::ggplotly(pl)
```


```{r}
orig.RNA.seu <- as.Seurat(orig.RNA)
orig.RNA.seu <- FindVariableFeatures(orig.RNA.seu)
orig.RNA.seu <- ScaleData(orig.RNA.seu)
orig.RNA.seu <- RunPCA(orig.RNA.seu)
orig.RNA.seu <- RunUMAP(orig.RNA.seu, dims=1:30)

DimPlot(orig.RNA.seu, group.by="annotation", label = TRUE)
```

## Prediction score
Quantifies the uncertainty of the prediction. Calculated differently for every method, but used to define which cells are "unassigned".


```{r}
orig.composition <- orig.RNA$annotation
orig.frac <- table(orig.composition)/length(orig.composition)

orig.frac.df <- data.frame(orig.frac) %>%
  dplyr::rename(predicted.id=orig.composition, frac.label=Freq) %>%
  mutate(method="original.RNA")

score_cols <- str_subset(colnames(atac.seu@meta.data), 'score_')
label_cols <- str_subset(colnames(atac.seu@meta.data), 'predicted.id_')

pred.labels.df <- imap(list(CCA=pred.cca, Liger=pred.liger, Conos=pred.conos), ~ 
      rownames_to_column(.x, "cell") %>%
      rename_all(funs(str_remove(., str_c("_",.y)))) %>%
      mutate(method=.y)
    ) %>%
  purrr::reduce(bind_rows) %>%
  mutate(score=ifelse(is.na(score), 0, score))

predict_score_hist <- 
  pred.labels.df %>%
  ggplot(aes(score, fill=method)) +
  geom_histogram(position="identity", alpha=0.8, bins=40) +
  facet_grid(method ~.) +
  scale_fill_brewer(palette="Set1") +
  xlab("Label prediction score") +
  theme_bw(base_size = 16) +
  theme(legend.position = "top")

cutoffs <- seq(0,1,0.05)
predict_score_cumedist <-
  pred.labels.df %>%
  group_by(method) %>%
  mutate(bins=cut(score, breaks = cutoffs)) %>%
  mutate(score=as.numeric(str_remove_all(as.character(bins), ".+,|]"))) %>%
  ggplot(aes(score, color=method)) +
  stat_ecdf(size=0.8, alpha=0.7) +
  scale_color_brewer(palette = "Set1") +
  ylab("Fraction of unassigned cells") +
  xlab("Prediction score cutoff") +
  theme_bw(base_size = 16) +
  xlim(0,1) +
  coord_fixed() +
  guides(color="none") 

ggarrange(predict_score_hist, predict_score_cumedist, common.legend = TRUE, widths = c(0.8, 1.2),
          labels=c("A", "B")) +
  ggsave(paste0(outdir, "prediction_score_distribution.pdf"), height = 6, width = 10)
```

```{r, fig.width=16, fig.height=8}
ggpubr::ggarrange(
  plotlist = list(
    FeaturePlot(atac.seu, reduction = "umap.snap", feature = "score_CCA"  , coord.fixed = TRUE) + ggtitle("CCA"),
    FeaturePlot(atac.seu, reduction = "umap.snap", feature = "score_Liger", coord.fixed = TRUE) + ggtitle("Liger"),
    FeaturePlot(atac.seu, reduction = "umap.snap", feature = "score_Conos", coord.fixed = TRUE) + ggtitle("Conos")
  ),
  common.legend = TRUE, ncol=3, nrow=1
) +
  ggsave(paste0(outdir, "prediction_score_umaps.pdf"), height = 7, width=14)
```


## Cell type composition

Compare cell type fractions (w uncertainty)
```{r, fig.height=8, fig.width=8}

pred.labels.df %>%
  group_by(method) %>%
  drop_na() %>%
  mutate(tot.cells=n()) %>%
  ungroup() %>%
  group_by(method, predicted.id) %>%
  summarise(tot.label = n(), tot.cells = max(tot.cells), mean.score=mean(score)) %>%
  mutate(frac.label=tot.label/tot.cells) %>%
  # bind_rows(orig.frac.df) %>%
  ggplot(aes(method, predicted.id)) +
  geom_point(aes(color=mean.score, size=frac.label)) +
  scale_color_continuous() +
  scale_color_gradient(low ="grey", high="blue") +
  theme_classic(base_size = 16)
  

```

Does the uncertainty depend on the size of the cluster?
```{r, fig.width=14, fig.height=5}

pred.labels.df %>%
  group_by(method) %>%
  drop_na() %>%
  mutate(tot.cells=n()) %>%
  ungroup() %>%
  group_by(method, predicted.id) %>%
  summarise(tot.label = n(), tot.cells = max(tot.cells), mean.score=median(score), sd.score=mad(score)) %>%
  mutate(frac.label=tot.label/tot.cells) %>%
  # bind_rows(orig.frac.df) %>%
  ggplot(aes(frac.label, mean.score, color=method)) +
  geom_point(size=2) +
  geom_errorbar(aes(ymin=mean.score-sd.score, ymax=mean.score+sd.score), alpha=0.6) +
  scale_color_brewer(palette="Set1") +
  facet_grid(. ~ method) +
  theme_bw(base_size = 16)
  
```

original size?
```{r, fig.width=14, fig.height=6}

pred.labels.df %>%
  group_by(method) %>%
  drop_na() %>%
  mutate(tot.cells=n()) %>%
  ungroup() %>%
  group_by(method, predicted.id) %>%
  summarise(tot.label = n(), tot.cells = max(tot.cells), mean.score=median(score), sd.score=mad(score)) %>%
  mutate(frac.label=tot.label/tot.cells) %>%
  left_join(select(orig.frac.df, predicted.id, frac.label), by="predicted.id", suffix=c(".int",".original")) %>%
  # bind_rows(orig.frac.df) %>%
  ggplot(aes(frac.label.original, mean.score, color=method)) +
  geom_point(size=2) +
  geom_errorbar(aes(ymin=mean.score-sd.score, ymax=mean.score+sd.score), alpha=0.6) +
  scale_color_brewer(palette="Set1") +
  facet_grid(.~method) +
  theme_bw(base_size = 16)
```




## Scoring per cell type
```{r, fig.height=7, fig.width=6}

dodge <- position_dodge(width=0.9)
pred.labels.df %>%
  group_by(method, predicted.id) %>%
  summarise(score.mean=mean(score, na.rm=F), score.cv = sd(score)/mean(score), n=n()) %>%
  # ggplot(aes(predicted.id, score.mean, fill=method)) +
  # geom_col( position = dodge) +
  ggplot(aes(predicted.id, method)) +
  geom_point(aes(size=score.mean, color=score.mean)) +
  scale_color_gradient(low="white", high = "blue") +
  # geom_errorbar(aes(ymin=score.mean-score.cv, ymax=score.mean+score.cv), position=dodge, width=0.25)  +
  coord_flip() +
  theme_bw(base_size = 16)

pred.labels.df %>%
  ggplot(aes(predicted.id, score, color=method)) +
  geom_boxplot() +
  coord_flip() +
  scale_color_brewer(palette="Set1")

```

### Agreement with unsupervised clustering of ATAC data
Calculate which fractions of NNs in bin based graph of ATAC cells have the same annotation
```{r}
k = 50
atac.seu <- FindNeighbors(atac.seu, assay = "ATAC", reduction = "SnapATAC", dims = 1:15, k.param = k)

atac.nn.list <- getNNlist(atac.seu)

score.CCA <- imap_dbl(atac.nn.list, ~ sum(pred.cca[.x,1] == pred.cca[.y,1])/k) %>% setNames(names(atac.nn.list))
score.Conos <- imap_dbl(atac.nn.list, ~ sum(pred.conos[.x,1] == pred.conos[.y,1])/k) %>% setNames(names(atac.nn.list))
score.Liger <- imap_dbl(atac.nn.list, ~ sum(pred.liger[.x,1] == pred.liger[.y,1])/k) %>% setNames(names(atac.nn.list))

knn_score_df <-
  as.data.frame(cbind(score.Conos, score.Liger, score.CCA)) %>%
  rownames_to_column("cell") %>%
  pivot_longer(cols=str_subset(colnames(.), "score"), names_to = "method", values_to = "KNN_score") %>%
  dplyr::mutate(KNN_score=ifelse(is.na(KNN_score), 0, KNN_score),
                method=str_remove(method, "score."))

quants = seq(0,1, by = 0.05)
AUECDF_knn_score <- knn_score_df %>%
  split(.$method) %>%
  map_dbl( ~ .x %>%
      arrange(KNN_score) %>% 
      {ecdf(.$KNN_score)(quants)} %>% AUC(quants,.)
    )
  
knn_score_df %>%
  mutate(AUC=AUECDF_knn_score[method]) %>%
  ggplot(aes(KNN_score, color=method, fill=method)) +
  stat_ecdf(size=1) +
  scale_color_brewer(palette = "Set1") +
  geom_text(data=. %>% group_by(method) %>% summarise(AUC=max(AUC)), 
            x=0.05, hjust=0,
            aes(label=glue("AUECDF = {round(AUC, 3)}"), y=c(0.90, 0.95, 1)))
```

```{r, fig.height=8, fig.width=8}

full_join(pred.labels.df, knn_score_df) %>%
  ggplot(aes(KNN_score, color=method)) +
  stat_ecdf() +
  facet_wrap("predicted.id") +
  scale_color_brewer(palette = "Set1") +
  coord_fixed()
```
```{r, fig.height=8, fig.width=7, message=FALSE}
plot_KNNecdf <- function(cluster){
  full_join(pred.labels.df, knn_score_df) %>%
    filter(predicted.id==cluster) %>%
    ggplot(aes(KNN_score, color=method)) +
    stat_ecdf(size=0.8) +
    facet_wrap("predicted.id") +
    xlim(0,1) + ylim(0,1) +
    coord_fixed() +
    scale_color_brewer(palette = "Set1") +
    theme_bw(base_size = 16) +
    theme(legend.position = "top")
}

DimPlotCluster <- function(annotation_col, cluster, label){
  highlight = which(atac.seu@meta.data[,annotation_col]==cluster)
  DimPlot(atac.seu, reduction = "umap.snap",cells.highlight = highlight, cols.highlight = "red", pt.size = 0.02, sizes.highlight = 0.1) +
    guides(color="none") +
    ggtitle(label = label)
  }

UMAPs_cluster <- function(cluster){
  ggarrange(plotlist=imap(list(CCA="predicted.id_CCA", Conos="predicted.id_Conos", Liger="predicted.id_Liger"), ~ DimPlotCluster(.x, cluster, label = .y )), ncol=3, nrow=1) %>% annotate_figure(cluster)
}

map(cell.types, ~ ggarrange(plot_KNNecdf(.x), UMAPs_cluster(.x), nrow = 2, heights = c(1,0.8)))

```


#### Which cells are inconsistently aligned?
```{r, fig.width=14, fig.height=10}
pred.labels.df %>%
  select(method, predicted.id, cell) %>%
  mutate(predicted.id=ifelse(is.na(predicted.id), "none", predicted.id)) %>%
  ggplot(aes(x=method, stratum=predicted.id, alluvium=cell, fill=predicted.id, label=predicted.id)) +
  geom_flow() +
  geom_stratum(color=NA) +
  geom_text(stat="stratum") +
  theme_bw(base_size = 16)
```


## Closer look at Liger
```{r}
library(liger)

plotByDatasetAndCluster(model.liger$model, clusters=c(set_names(as.character(orig.RNA$annotation), colnames(orig.RNA)), set_names(as.character(pred.liger[,'predicted.id_Liger']), rownames(pred.liger))))
```

## Compare feature selection strategy
```{r}
model.cca.union <- readRDS("~/models/modelCCA_union_hvg_PBMC_SCElist_20191105.RDS")
seu.cca.union <- readRDS("~/models/labelTransferCCA_union_hvg_PBMC_SCElist_20191105.RDS")
seu.liger.union <- readRDS("~/models/labelTransferLiger_union_hvg_PBMC_SCElist_20191105.RDS")
seu.conos.union <- readRDS("~/models/labelTransferConos_union_hvg_PBMC_SCElist_20191105.RDS")

integrate_features <- model.cca.union$model@anchor.features

int.list <- list(CCA=seu.cca, Liger=seu.liger, Conos=seu.conos)

## Add to ATAC object meta.data
pred.cca.union <- getPredictedLabels(seu.cca.union, "CCA_union", score.col = "prediction.score.max")
pred.liger.union<- getPredictedLabels(seu.liger.union, "Liger_union")
pred.conos.union<- getPredictedLabels(seu.conos.union, "Conos_union")

# pred.cca.union <- getPredictedLabels(seu.cca.union, "CCA.union", score.col = "prediction.score.max")
# cbind(pred.cca, pred.cca.union)


if (all(rownames(pred.conos) == rownames(pred.cca)) & all(rownames(pred.conos) == rownames(pred.liger))) {
  atac.seu <- AddMetaData(atac.seu, metadata = cbind(pred.cca.union, pred.liger.union, pred.conos.union))
} else {
  stop("Non corresponding cell names")
}

```

```{r, fig.width=10, fig.height=14}
ggpubr::ggarrange(
  plotlist = list(
    DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_CCA"  , cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("CCA"),     DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_CCA_union"  , cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("CCA union"),
    DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_Liger", cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("Liger"),
        DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_Liger_union", cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("Liger_union"),
    DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_Conos", cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("Conos"),
    DimPlot(atac.seu, reduction = "umap.snap", group.by = "predicted.id_Conos_union", cols=cell.type.pal, label=TRUE, repel=TRUE) + ggtitle("Conos union")
      ),
  common.legend = TRUE, ncol=2, nrow=3
)
```

```{r, fig.width=16, fig.height=8}
ggpubr::ggarrange(
  plotlist = list(
    FeaturePlot(atac.seu, reduction = "umap.snap", feature = "score_CCA_union"  , coord.fixed = TRUE) + ggtitle("CCA"),
    FeaturePlot(atac.seu, reduction = "umap.snap", feature = "score_Liger_union", coord.fixed = TRUE) + ggtitle("Liger"),
    FeaturePlot(atac.seu, reduction = "umap.snap", feature = "score_Conos_union", coord.fixed = TRUE) + ggtitle("Conos")
  ),
  common.legend = TRUE, ncol=3, nrow=1
) 
```

```{r}
orig.composition <- orig.RNA$annotation
orig.frac <- table(orig.composition)/length(orig.composition)

orig.frac.df <- data.frame(orig.frac) %>%
  dplyr::rename(predicted.id=orig.composition, frac.label=Freq) %>%
  mutate(method="original.RNA")

score_cols_union <- str_subset(colnames(atac.seu@meta.data), 'score_.+_union')
label_cols_union <- str_subset(colnames(atac.seu@meta.data), 'predicted.id_.+union')

pred.labels.union.df <- imap(list(CCA=pred.cca.union, Liger=pred.liger.union, Conos=pred.conos.union), ~ 
      rownames_to_column(.x, "cell") %>%
      rename_all(funs(str_remove(., str_c("_",.y)))) %>%
      mutate(method=.y)
    ) %>%
  purrr::reduce(bind_rows) %>%
  mutate(score=ifelse(is.na(score_union), 0, score_union))

predict_score_hist <- 
  pred.labels.union.df %>%
  ggplot(aes(score_union, fill=method)) +
  geom_histogram(position="identity", alpha=0.8, bins=40) +
  facet_grid(method ~.) +
  scale_fill_brewer(palette="Set1") +
  xlab("Label prediction score") +
  theme_bw(base_size = 16) +
  theme(legend.position = "top")

cutoffs <- seq(0,1,0.05)
predict_score_cumedist <-
  pred.labels.union.df %>%
  group_by(method) %>%
  mutate(bins=cut(score_union, breaks = cutoffs)) %>%
  mutate(score=as.numeric(str_remove_all(as.character(bins), ".+,|]"))) %>%
  ggplot(aes(score, color=method)) +
  stat_ecdf(size=0.8, alpha=0.7) +
  scale_color_brewer(palette = "Set1") +
  ylab("Fraction of unassigned cells") +
  xlab("Prediction score cutoff") +
  theme_bw(base_size = 16) +
  xlim(0,1) +
  coord_fixed() +
  guides(color="none") 

ggarrange(predict_score_hist, predict_score_cumedist, common.legend = TRUE, widths = c(0.8, 1.2),
          labels=c("A", "B")) 
  ggsave(paste0(outdir, "prediction_score_distribution.pdf"), height = 6, width = 10)
```
```{r}
k = 50
atac.seu <- FindNeighbors(atac.seu, assay = "ATAC", reduction = "SnapATAC", dims = 1:15, k.param = k)

atac.nn.list <- getNNlist(atac.seu)

calculate_KNN_agreement <- function(pred.cca, pred.conos, pred.liger, k=50){
  score.CCA <- imap_dbl(atac.nn.list, ~ sum(pred.cca[.x,1] == pred.cca[.y,1])/k) %>% setNames(names(atac.nn.list))
  score.Conos <- imap_dbl(atac.nn.list, ~ sum(pred.conos[.x,1] == pred.conos[.y,1])/k) %>% setNames(names(atac.nn.list))
  score.Liger <- imap_dbl(atac.nn.list, ~ sum(pred.liger[.x,1] == pred.liger[.y,1])/k) %>% setNames(names(atac.nn.list))
  
  knn_score_df <-
    as.data.frame(cbind(score.Conos, score.Liger, score.CCA)) %>%
    rownames_to_column("cell") %>%
    pivot_longer(cols=str_subset(colnames(.), "score"), names_to = "method", values_to = "KNN_score") %>%
    dplyr::mutate(KNN_score=ifelse(is.na(KNN_score), 0, KNN_score),
                  method=str_remove(method, "score."))
  
  quants = seq(0,1, by = 0.05)
  AUECDF_knn_score <- knn_score_df %>%
    split(.$method) %>%
    map_dbl( ~ .x %>%
        arrange(KNN_score) %>% 
        {ecdf(.$KNN_score)(quants)} %>% AUC(quants,.)
      )
  list(knn_score_df, AUECDF_knn_score)  
}
  
knn_agreement <- calculate_KNN_agreement(pred.cca.union, pred.conos.union, pred.liger.union, k=50)
knn_agreement[[1]] %>%
  mutate(AUC=knn_agreement[[2]][method]) %>%
  ggplot(aes(KNN_score, color=method, fill=method)) +
  stat_ecdf(size=1) +
  scale_color_brewer(palette = "Set1") +
  geom_text(data=. %>% group_by(method) %>% summarise(AUC=max(AUC)), 
            x=0.05, hjust=0,
            aes(label=glue("AUECDF = {round(AUC, 3)}"), y=c(0.90, 0.95, 1)))
```

### Thoughts
- Conos scores a lot of cells with high confidence, but fails to assign cells to difficult clusters 
- CCA resembles the composition of the RNA data better, but curious that the other methods identify way more 










